gss-polviews-dive
Zach Gozlan
December 13, 2019
This is version #0, testing github pages functionality with r studio
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.5.3## Warning: As of rlang 0.4.0, dplyr must be at least version 0.8.0.
## x dplyr 0.7.8 is too old for rlang 0.4.2.
## i Please update dplyr to the latest version.
## i Updating packages on Windows requires precautions:
## <https://github.com/jennybc/what-they-forgot/issues/62>library(plotly)## Warning: package 'plotly' was built under R version 3.5.3##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
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## layoutrequire(tidyverse)## Loading required package: tidyverse## Warning: package 'tidyverse' was built under R version 3.5.2## -- Attaching packages --------------------------------------------------------------------------------------------------------------------------- tidyverse 1.2.1 --## √ tibble 1.4.2 √ purrr 0.2.5
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## √ readr 1.1.1 √ stringr 1.3.1
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## x dplyr::filter() masks plotly::filter(), stats::filter()
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library(foreign)
library(haven)
library(plyr)## Warning: package 'plyr' was built under R version 3.5.2## -------------------------------------------------------------------------## You have loaded plyr after dplyr - this is likely to cause problems.
## If you need functions from both plyr and dplyr, please load plyr first, then dplyr:
## library(plyr); library(dplyr)## -------------------------------------------------------------------------##
## Attaching package: 'plyr'## The following objects are masked from 'package:dplyr':
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## arrange, count, desc, failwith, id, mutate, rename, summarise,
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## arrange, mutate, rename, summarisegss_means = read_csv('birthcohort_means.csv')## Parsed with column specification:
## cols(
## survey_yr = col_integer(),
## birth_cohort = col_double(),
## counts = col_integer(),
## age = col_double(),
## polviews_wmean = col_double(),
## happy_wmean = col_double(),
## conrinc_wmean = col_double(),
## dwelown_wmean = col_double(),
## been_married_wmean = col_double(),
## conrinc_median = col_double(),
## is2018 = col_integer(),
## been_married_wmean_binned = col_double()
## )gss_means = gss_means[!gss_means$survey_yr == 1983,] #realized i should drop this too - sparse
head(gss_means)## # A tibble: 6 x 12
## survey_yr birth_cohort counts age polviews_wmean happy_wmean
## <int> <dbl> <int> <dbl> <dbl> <dbl>
## 1 1974 1900 69 74 3.93 1.61
## 2 1974 1905 97 69 4.26 1.62
## 3 1974 1910 80 64 4.08 1.54
## 4 1974 1915 107 59 4.36 1.73
## 5 1974 1920 123 54 4.21 1.66
## 6 1974 1925 105 49 4.15 1.72
## # ... with 6 more variables: conrinc_wmean <dbl>, dwelown_wmean <dbl>,
## # been_married_wmean <dbl>, conrinc_median <dbl>, is2018 <int>,
## # been_married_wmean_binned <dbl>polviews_survey_yr <- ggplot(data=gss_means[gss_means$survey_yr > 1984,], aes(x=age, y=dwelown_wmean, colour=as.factor(birth_cohort))) +
geom_line()
#ggsave('polviews_survey_yr_test1.png', plot = polviews_survey_yr, width = 20, height = 6)
ggplotly(polviews_survey_yr)## Warning: The `printer` argument is deprecated as of rlang 0.3.0.
## This warning is displayed once per session.## This version of Shiny is designed to work with 'htmlwidgets' >= 1.5.
## Please upgrade via install.packages('htmlwidgets').polviews_age <- ggplot(data=gss_means, aes(x=as.factor(survey_yr), y=birth_cohort, fill=polviews_wmean, text = paste("Sample Size:", counts, "\n Age:", age))) +
geom_tile() + scale_fill_gradient(low="orange", high="blue") + theme(axis.text.x = element_text(angle=45))
#ggsave('polviews_age_test1.png', plot = polviews_age, width = 10, height = 6)
ggplotly(polviews_age, tooltip=c("x", "y", "fill", "text"))## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'polviews_married <- ggplot(data=gss_means, aes(x=as.factor(survey_yr), y=birth_cohort, fill=as.factor(been_married_wmean_binned), text = paste("Sample Size:", counts, "\n Age:", age, "\n Marriage%:", been_married_wmean))) + scale_fill_brewer(palette="Spectral") +
geom_tile() + theme(axis.text.x = element_text(angle=45))
#ggsave('polviews_age_test1.png', plot = polviews_age, width = 10, height = 6)
ggplotly(polviews_married, tooltip=c("x", "y", "text"))polviews_age <- ggplot(data=gss_means, aes(x=age, y=as.factor(survey_yr), fill=conrinc_median, text = paste("Sample Size:", counts, "\n Age:", age))) +
geom_tile() + scale_fill_gradient(low="orange", high="blue") + theme(axis.text.x = element_text(angle=45))
#ggsave('polviews_age_test1.png', plot = polviews_age, width = 10, height = 6)
ggplotly(polviews_age, tooltip=c("x", "y", "fill", "text"))## Warning: 'heatmap' objects don't have these attributes: 'showlegend'
## Valid attributes include:
## 'type', 'visible', 'opacity', 'name', 'uid', 'ids', 'customdata', 'meta', 'hoverinfo', 'hoverlabel', 'stream', 'transforms', 'uirevision', 'z', 'x', 'x0', 'dx', 'y', 'y0', 'dy', 'text', 'hovertext', 'transpose', 'xtype', 'ytype', 'zsmooth', 'connectgaps', 'xgap', 'ygap', 'zhoverformat', 'hovertemplate', 'zauto', 'zmin', 'zmax', 'zmid', 'colorscale', 'autocolorscale', 'reversescale', 'showscale', 'colorbar', 'coloraxis', 'xcalendar', 'ycalendar', 'xaxis', 'yaxis', 'idssrc', 'customdatasrc', 'metasrc', 'hoverinfosrc', 'zsrc', 'xsrc', 'ysrc', 'textsrc', 'hovertextsrc', 'hovertemplatesrc', 'key', 'set', 'frame', 'transforms', '_isNestedKey', '_isSimpleKey', '_isGraticule', '_bbox'ggplot(data=gss_means, aes(x=age, y=conrinc_median, colour=as.factor(is2018))) +
geom_point()
test_gen <- subset(gss_means, birth_cohort==1950)
ggplot(data=test_gen, aes(x=survey_yr, y=polviews_wmean)) +
geom_line()